Spaces:
Sleeping
Sleeping
| import os | |
| import gradio as gr | |
| import requests | |
| import json | |
| # Get the Hugging Face API key from Spaces secrets. | |
| HF_API_KEY = os.getenv("HF_API_KEY") | |
| # Model endpoints on Hugging Face | |
| MODEL_ENDPOINTS = { | |
| "Qwen2.5-72B-Instruct": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct", | |
| "Llama3.3-70B-Instruct": "https://api-inference.huggingface.co/models/meta-llama/Llama-3.3-70B-Instruct", | |
| "Qwen2.5-Coder-32B-Instruct": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-32B-Instruct", | |
| } | |
| # System prompts for each model | |
| SYSTEM_PROMPTS = { | |
| "Qwen2.5-72B-Instruct": "System: You are a knowledgeable assistant for general inquiries.", | |
| "Llama3.3-70B-Instruct": "System: You are a research expert assistant specialized in in-depth analysis.", | |
| "Qwen2.5-Coder-32B-Instruct": "System: You are a coding expert who helps with code-related tasks.", | |
| } | |
| def query_model(prompt, model_endpoint, system_prompt): | |
| headers = { | |
| "Authorization": f"Bearer {HF_API_KEY}", | |
| "Content-Type": "application/json", | |
| "Accept": "application/json" | |
| } | |
| # Combine the system prompt with the user prompt | |
| formatted_prompt = f"{system_prompt}\nUser: {prompt}\nAssistant:" | |
| data = { | |
| "inputs": formatted_prompt, | |
| "parameters": { | |
| "max_new_tokens": 512, | |
| "temperature": 0.6, # All models use a temperature of 0.6 | |
| } | |
| } | |
| response = requests.post(model_endpoint, headers=headers, json=data) | |
| # Uncomment the following line to print the raw API response for debugging: | |
| # print("Raw response:", response.text) | |
| try: | |
| result = response.json() | |
| except Exception: | |
| return f"Error: Unable to parse JSON. Response: {response.text}" | |
| if isinstance(result, dict) and "error" in result: | |
| return f"Error: {result['error']}" | |
| try: | |
| return result[0].get("generated_text", "No generated_text found in response") | |
| except Exception: | |
| return f"Error: Unexpected response format: {json.dumps(result)}" | |
| def chat_with_models(user_input, history): | |
| responses = [] | |
| for model_name, endpoint in MODEL_ENDPOINTS.items(): | |
| system_prompt = SYSTEM_PROMPTS.get(model_name, "") | |
| model_response = query_model(user_input, endpoint, system_prompt) | |
| responses.append(f"**{model_name}**: {model_response}") | |
| combined_answer = "\n\n".join(responses) | |
| history.append((user_input, combined_answer)) | |
| return history, history | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Multi-LLM Chatbot using Hugging Face Inference API") | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox(label="Your Message") | |
| clear = gr.Button("Clear") | |
| def clear_chat(): | |
| return [], [] | |
| msg.submit(fn=chat_with_models, inputs=[msg, chatbot], outputs=[chatbot, chatbot]) | |
| clear.click(fn=clear_chat, outputs=[chatbot, chatbot]) | |
| demo.launch() | |